Predicting Intelligibility Gains in Dysarthria through Automated Speech Feature Analysis

Autor/inn/en	Fletcher, Annalise R.; Wisler, Alan A.; McAuliffe, Megan J.; Lansford, Kaitlin L.; Liss, Julie M.
Titel	Predicting Intelligibility Gains in Dysarthria through Automated Speech Feature Analysis
Quelle	In: Journal of Speech, Language, and Hearing Research, 60 (2017) 11, S.3058-3068 (11 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
DOI	10.1044/2017_JSLHR-S-16-0453
Schlagwörter	Acoustics; Prediction; Older Adults; Oral Reading; Models; Evaluation Methods; Articulation Impairments; Comprehension; Pronunciation + Suchen Sie Ihr Suchwort? Akustik; Vorhersage; Älterer Erwachsener; Oral work; Reading; Mündliche Übung; Leseprozess; Lesen; Analogiemodell; Artikulationsstörung; Verstehen; Verständnis; Aussprache
Abstract	Purpose: Behavioral speech modifications have variable effects on the intelligibility of speakers with dysarthria. In the companion article, a significant relationship was found between measures of speakers' baseline speech and their intelligibility gains following cues to speak louder and reduce rate (Fletcher, McAuliffe, Lansford, Sinex, & Liss, 2017). This study reexamines these features and assesses whether automated acoustic assessments can also be used to predict intelligibility gains. Method: Fifty speakers (7 older individuals and 43 with dysarthria) read a passage in habitual, loud, and slow speaking modes. Automated measurements of long-term average spectra, envelope modulation spectra, and Mel-frequency cepstral coefficients were extracted from short segments of participants' baseline speech. Intelligibility gains were statistically modeled, and the predictive power of the baseline speech measures was assessed using cross-validation. Results: Statistical models could predict the intelligibility gains of speakers they had not been trained on. The automated acoustic features were better able to predict speakers' improvement in the loud condition than the manual measures reported in the companion article. Conclusions These acoustic analyses present a promising tool for rapidly assessing treatment options. Automated measures of baseline speech patterns may enable more selective inclusion criteria and stronger group outcomes within treatment studies. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01